iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://doi.org/10.1007/s11277-018-5327-z
Study on Image Denoising Method Based on Multiple Parameter Shrinkage Function | Wireless Personal Communications Skip to main content
Springer Nature Link
Log in

Study on Image Denoising Method Based on Multiple Parameter Shrinkage Function

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In transform based image denoising methods, how to modify the transform coefficients is an important problem. In wavelet image denoising, two-dimensional tensor product wavelet has isotropy, with poor selectivity, making it difficult to describe the high dimensional geometric features of images. With the development of multi-scale transform, Contourlet transform is emerging prominently. In this study, the advantages of soft threshold and hard threshold shrinkage functions are combined and a multiple parameter shrinkage function (MPSF) is proposed for image denoising. To verify the effectiveness of MPSF, it is used to denoise images polluted by Gaussian white noise. Experimental results show that the proposed shrinkage function is effective, and the denoised images have satisfactory visual effect, with significantly improved image quality metrics such as peak signal to noise ratio.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Zhou, J., Dai, R., & Xiao, B. (2008). Overview of image quality assessment research. Computer Science, 35(7), 1–4.

    Google Scholar 

  2. Xiaohong, W. (2012). Image denoising based on wavelet transform and median filter. Journal of Beihua University Natural Science, 13(3), 352–355.

    Google Scholar 

  3. Do, M. N., & Vetterli, M. (2003). The finite ridgelet transform for image representation. IEEE Transactions on Image Processing, 12(1), 16–28.

    Article  MathSciNet  MATH  Google Scholar 

  4. Alam, M. J., & Shaughnessy, D. O. (2011). Perceptual improvement of Wiener filtering employing a post-filter. Digital Signal Processing, 21, 54–65.

    Article  Google Scholar 

  5. Do, M. N., & Vetterli, M. (2005). The Contourlet transform: An efficient directional multi resolution image representation. IEEE Transactions on Image Processing, 14(12), 2091–2106.

    Article  Google Scholar 

  6. Deng, K., Zhang, L., & Luo, M. K. (2011). A denoising algorithm for noisy chaotic signals based on the higher order threshold function in wavelet-packet. Chinese Physics Letters, 28(2), 020502.

    Article  Google Scholar 

  7. Fang, Y. M., Feng, H. L., Li, J., et al. (2011). Stress wave signal denoising using ensemble empirical mode decomposition and an instantaneous half period model. Sensors, 11, 7554–7567.

    Article  Google Scholar 

  8. Yang, H.-Y., Zhang, N., Wang, X.-Y., & Zhang, Y. (2016). RHFMs similarity based nonlocal means image denoising in PDTDFB domain. Optik, 127, 1034–1036.

    Article  Google Scholar 

  9. Xiang-yang, W., Wei-wei, S., Zhi-fang, W., et al. (2015). Color image segmentation using PDTDFB domain hidden Markov tree model. Applied Soft Computing, 29, 138–152.

    Article  Google Scholar 

  10. Christos, Y., Jedrzej, M., Konstantinos, R., et al. (2014). Multicomponent decomposition of a time-varying acoustic Doppler signal generated by a passing railway vehicle using complex shifted morlet wavelets. Transportation Research Part C: Emerging Technologies, 44, 34–51.

    Article  Google Scholar 

  11. Singh, R., & Khare, A. (2014). Fusion of multimodal medical images using Daubechies complex wavelet transform—A multiresolution approach. Information Fusion, 19, 49–60.

    Article  Google Scholar 

  12. Zhao, Y., Xu, D., Qian, W., et al. (2016). Fast image blending using run-length encoding and SIMD instruction set. Journal of Computer Aided Design & Computer Graphics, 28(4), 623–631.

    Google Scholar 

  13. Do, M. N., & Vetteri, M. (2003). Framing pyramids. IEEE Transactions on Signal Processing, 51(9), 2329–2342.

    Article  MathSciNet  MATH  Google Scholar 

  14. Jing-Bin, W., & Bao, W. (2014). An adaptive de-noising method via the lifting scheme. Journal of Algorithms & Computational Technology, 8, 27–44.

    Article  Google Scholar 

  15. Shen, Y., Dang, J., Wang, Y., et al. (2015). Infrared and visible light image fusion algorithm based on compressed sensing. Journal of Information and Computational Science, 12, 1337–1347.

    Article  Google Scholar 

  16. Donoho, D. (1995). De-noising by soft-thresholding. IEEE Transactions on Information Theory, 41(3), 613–626.

    Article  MathSciNet  MATH  Google Scholar 

  17. Chinnarao, B., & Adhavilatha, M. (2012). Improved image denoising algorithm using dual tree complex wavelet transform. International Journal of Computer Applications, 44(20), 1–6.

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the Fundamental Research Funds for the Central Universities (No. 2016MS151).

Author information

Authors and Affiliations

  1. School of Control and Computer Engineering, North China Electric Power University, Baoding, China

    Wei Xiong, Ze Wang & Hejin Yuan

  2. Japan–China AI-IoT Industry Alliance, Tokyo, Japan

    Jin Liu

Authors
  1. Wei Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ze Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hejin Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei Xiong.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiong, W., Wang, Z., Yuan, H. et al. Study on Image Denoising Method Based on Multiple Parameter Shrinkage Function. Wireless Pers Commun 102, 3079–3088 (2018). https://doi.org/10.1007/s11277-018-5327-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-018-5327-z

Keywords

Search

Navigation